Python 重塑dataframe以将分类列转换为单个列

我有如下数据：

df = pd.DataFrame(data=[list('ABCDE'), 
          ['Crude Oil', 'Natural Gas', 'Gasoline', 'Diesel', 'Bitumen'],
          ['Natural Gas', 'Salt water', 'Waste water', 'Motor oil', 'Sour Gas'],
          ['Oil', 'Gas', 'Refined', 'Refined', 'Oil'],
          ['Gas', 'Water', 'Water', 'Oil', 'Gas'],
          list(np.random.randint(10, 100, 5)),
          list(np.random.randint(10, 100, 5))]
          ).T
df.columns =['ID', 'Substance1', 'Substance2', 'Category1', 'Category2', 'Quantity1', 'Quantity2']

  ID   Substance1  Substance2 Category1 Category2 Quantity1 Quantity2
0  A    Crude Oil  Natural Gas      Oil       Gas        85        14
1  B  Natural Gas   Salt water      Gas     Water        95        78
2  C     Gasoline  Waste water  Refined     Water        33        25
3  D       Diesel    Motor oil  Refined       Oil        49        54
4  E      Bitumen     Sour Gas      Oil       Gas        92        86

  ID   Oil  Gas Water Refined
0  A    85   14   NaN     NaN
1  B   NaN   95    78     NaN
2  C   NaN  NaN    25      33
3  D    54  NaN   NaN      49  
4  E    92   86   NaN     NaN

类别

和

数量

列是指相应的

物质

列

我想将

类别

列展开为每个唯一值的新列，并将

数量

值作为单元格值。不存在的类别将是NaN。因此，生成的帧将如下所示：

df = pd.DataFrame(data=[list('ABCDE'), 
          ['Crude Oil', 'Natural Gas', 'Gasoline', 'Diesel', 'Bitumen'],
          ['Natural Gas', 'Salt water', 'Waste water', 'Motor oil', 'Sour Gas'],
          ['Oil', 'Gas', 'Refined', 'Refined', 'Oil'],
          ['Gas', 'Water', 'Water', 'Oil', 'Gas'],
          list(np.random.randint(10, 100, 5)),
          list(np.random.randint(10, 100, 5))]
          ).T
df.columns =['ID', 'Substance1', 'Substance2', 'Category1', 'Category2', 'Quantity1', 'Quantity2']

  ID   Substance1  Substance2 Category1 Category2 Quantity1 Quantity2
0  A    Crude Oil  Natural Gas      Oil       Gas        85        14
1  B  Natural Gas   Salt water      Gas     Water        95        78
2  C     Gasoline  Waste water  Refined     Water        33        25
3  D       Diesel    Motor oil  Refined       Oil        49        54
4  E      Bitumen     Sour Gas      Oil       Gas        92        86

  ID   Oil  Gas Water Refined
0  A    85   14   NaN     NaN
1  B   NaN   95    78     NaN
2  C   NaN  NaN    25      33
3  D    54  NaN   NaN      49  
4  E    92   86   NaN     NaN

---

我尝试了

.melt（）

，然后是

.pivot\u table（）

，但由于某些原因，值会在新的类别列中重复。

您需要使用

pd.melt

然后使用

groupby

：

np.random.seed(0)

df = pd.DataFrame(data=[list('ABCDE'), 
          ['Crude Oil', 'Natural Gas', 'Gasoline', 'Diesel', 'Bitumen'],
          ['Natural Gas', 'Salt water', 'Waste water', 'Motor oil', 'Sour Gas'],
          ['Oil', 'Gas', 'Refined', 'Refined', 'Oil'],
          ['Gas', 'Water', 'Water', 'Oil', 'Gas'],
          list(np.random.randint(10, 100, 5)),
          list(np.random.randint(10, 100, 5))]
          ).T
df.columns =['ID', 'Substance1', 'Substance2', 'Category1', 'Category2', 'Quantity1', 'Quantity2']

pd.wide_to_long(df,['Substance','Category','Quantity'], 'ID','Num','','.+')\
  .groupby(['ID','Category'])['Quantity'].sum()\
  .unstack().reset_index()

输出：

Category ID   Gas   Oil  Refined  Water
0         A  19.0  54.0      NaN    NaN
1         B  57.0   NaN      NaN   93.0
2         C   NaN   NaN     74.0   31.0
3         D   NaN  46.0     77.0    NaN
4         E  97.0  77.0      NaN    NaN

---

这是可行的，但并非如此。它创建了大量重复的列，并添加了所有的数字，导致了不准确的值。但是在链中添加了两种方法，

reset\u index

和

drop\u duplicates

，

pd.wide\u to\u long（df，['Substance'，'Category'，'Quantity']，'Quantity']，'ID'，'Num'，'.+'）\.reset\u index（）.drop\u duplicates（子集=['ID'，'Num Num']）\.groupby（['ID'，'Category']）['Quantity'].sum（）.sum（）.unstack（）.reset\u index（）

我要补充的是，这个漂亮的解决方案还需要对较老的

熊猫进行一些调整

0.19。